1. KNOWLEDGE REPRESENTATION
Lecture 2
Knowledge Representation

2. Knowledge Representation: Principles and Techniques
Lexical ambiguity of natural language
“Visiting aunts can be a nuisance”
Need for common sense knowledge
“The hammer hit the vase and it broke”
“The vase hit the wall and it broke”
“the X hit the Y and it broke”
In each of the above, what does ‘it’ refer to ?
Lecture 2
Knowledge Representation

3. Predicate-argument Expression
<sentence><predicate>(<argument>,…,<argument>)
e.g.
at(robot, roomA)
Interpretation: Robot is located in Room A
(and not Room A is located at the robot)
Lecture 2
Knowledge Representation

4. Knowledge Representation
STRIPS Planner
push(X,Y,Z)
Preconditions: at(robot,Y), at(X,Y)
Delete list: at(robot,Y), at(X,Y)
Add list: at(robot,Z), at(X,Z)
move(X,Y)
Preconditions: at(robot,X)
Delete list: at(robot,X)
Add list: at(robot,Y)
Lecture 2
Knowledge Representation

5. STRIPS Planner in Action
Start
at(robot,roomA)
at(box1,roomB)
at(box2,roomC)
move(roomA,roomB)
X/roomA,Y/roomB
P: at(robot,roomA)
D: at(robot,roomA)
A: at(robot,roomB)
push(box1,roomB,roomA)
X/box1,Y/roomB,Z/roomA
P: at(robot,roomB)
P: at(box1,roomB)
D: at(robot,roomB)
D: at(box1,roomB)
A: at(robot,roomA)
A: at(box1,roomA)
move(roomA,roomC)
X/roomA,Y/roomC
A: at(robot,roomC)
push(box2,roomC,roomA)
X/box2,Y/roomC,Z/roomA
P: at(robot,roomC)
P: at(box2,roomC)
D: at(robot,roomC)
D: at(box2,roomC)
A: at(box2,roomA)
Goal
at(box1,roomA)
at(box2,roomA)
Lecture 2
Knowledge Representation

6. Knowledge Representation
MYCIN
Domain: treatment of blood infection
Decision process:
Deciding if patient has significant infection
Determining possible organisms involved
Selecting a set of appropriate drugs
Choosing most appropriate drug(s)
Lecture 2
Knowledge Representation

7. Organization of MYCIN Consultation program Explanation program
Knowledge acquisition program
Static knowledge base
Dynamic patient
data
Physician user
Infectious disease expert
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Knowledge Representation

8. MYCIN’s Control StructureIF
1) there is an organism which requires therapy, and
2) consideration has been given to any other
organisms requiring therapy
THEN
compile a list of possible therapies, and determine the best one in this list.
Lecture 2
Knowledge Representation

9. Knowledge Representation
Consultation Session
Create the patient context as the top node in the context tree;
Attempt to apply the goal rule to this patient context.
Consultation is essentially a search through a tree of goals. The leaves of the tree as fact goals, such as laboratory data.
Lecture 2
Knowledge Representation

10. An example of MYCIN’s rules
IF 1) The stain of the organism is gramneg, and
2) The morphology of the organism is rod, and
3) The aerobicity of the organism is aerobic
THEN There is strongly suggestive evidence (.8) that
the class of the organism is enterobacteriaceae
Lecture 2
Knowledge Representation

11. Knowledge Representation
MYCIN Context Tree
Patient-1
Culture-1
Culture-2
Culture-3
Operation-1
Organism-1
Organism-2
Organism-3
Drug-1
Drug-2
Lecture 2
Knowledge Representation

12. Example data for organism-1
GRAM = (GRAMNEG 1.0)
MORPH = (ROD 0.8) (COCCUS 0.2)
AIR = (AEROBIC 0.6)
Considering the preceding rule,
CF(premise) = min(1.0 , 0.8 , 0.6) = 0.6
CF(action) = CF(premise) × CF(rule)
= 0.6 × 0.8 = 0.48
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Knowledge Representation

13. Knowledge Representation
AND/OR Tree
BADGE
SHIELD
GUN
POLICE
REVOLVER
PISTOL
RIFLE
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Knowledge Representation

14. Knowledge Representation
Evidence Combination
Let X and Y be weights derived from the application of two different rules. Combined certainty of both rules:
Lecture 2
Knowledge Representation

15. Knowledge Representation
Evaluation of MYCIN
Ratings by 8 experts on 10 cases
Perfect score = 80
MYCIN 52
Actual Therapy 46
Faculty-1 50
Faculty-4 44
Faculty-2 48
Resident 36
Inf dis fellow
Faculty-5 34
Faculty-3
Student 24
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Knowledge Representation

16. Comparing MYCIN and STRIPS
Both have goal-driven control structure
STRIPS is knowledge-poor (weak method)
MYCIN is domain specific (strong method)
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Knowledge Representation

17. Symbolic Representation
Syntactic rules to form symbol structures out of symbols
Transformation rules to turn symbol structures into other symbol structures
Programs in such languages are themselves symbol structures
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Knowledge Representation

18. Physical Symbol System Hypothesis
“A physical symbol system has the necessary and sufficient means for general intelligent action”
− Newell & Simon (1976)
Lecture 2
Knowledge Representation

19. Symbol Structures in LISP
LISP: LISt Processing
LISP differs from most programming languages in three important respects:
Its main data structure is the list;
Its programs are also represented by list structures; and
Its primitive operations are operations on lists.
Lecture 2
Knowledge Representation

20. Knowledge Representation
Lists and Dotted Pairs
Any atom is an S-expression
If A1 and A2 are S-expression, then (A1 . A2) is an S-expression
If S = (A1 . (A2 . (… . (An-1 . An)…) is an S-expression for n>0, then S is a list if and only if An = NIL
(S1 . (S2 . (S3 . NIL))) is simply (S1 S2 S3)
(A . B) is not a list. It is a dotted pair.
Lecture 2
Knowledge Representation

21. Knowledge Representation
LISP Programs
(<function> <1st argument> <2nd argument>)
e.g.
(+ X Y)
To treat a list as merely a data structure rather than a program, use “quote”
(quote X) or ’X
’( ( ) ( ) ( ) )
Lecture 2
Knowledge Representation

22. Function Definition in LISP
(defun <function name> (<formal parameters>)
<function body> )
e.g.
(defun SQUARE (X)
(* X X ) )
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Knowledge Representation

23. Processing Association Lists
(defun assoc (key alist)
(cond ((null alist) NIL)
((eq (first (first alist)) key) (first alist))
(t (assoc key (rest alist))))
(assoc ’alaska ’( (alabama montgomey)
(alaska juneau)
…) )
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Knowledge Representation

24. Knowledge Representation
Pattern Matching
(defun match (sample pattern)
(cond ((and (null sample) (null pattern)) T)
((or (null sample) (null pattern)) NIL)
((eq (first pattern) ’?)
(match (rest sample) (rest pattern) ) )
((eq (first sample) (first pattern) )
(T NIL) )
Lecture 2
Knowledge Representation

25. Pattern Matching (contd.)
(match ’(at robot room) ’(at robot ?) ) T
(match ’(at box room) ’(at robot ?) )
NIL
(match ’(at robot room) ’(at ? ?) )
Lecture 2
Knowledge Representation

26. Function Definition in CLIPS
(deffunction hypotenuse (?a ?b)
(sqrt (+ (* ?a ?a) (* ?b ?b) ) ) )
(hypotenuse 3 4)
5.0
Lecture 2
Knowledge Representation

27. CLIPS for Rule-based Systems
Freeware, available at:
Borrows features from other tools
Fairly standard LISP-like syntax
Reasonably efficient
Reasonably flexible
Facilities for combining rules with objects
Lecture 2
Knowledge Representation